Food Engineering
Sajad Jafarzadeh; Mohsen Azadbakht; Faryal Varasteh; Mohammad Vahedi Torshizi
Abstract
Since persimmon is a pressure-sensitive fruit and it is difficult to store this fruit in warehouses, in this research, an attempt has been made to examine the parameters affecting the reduction of changes in its physical properties. The samples were loaded at 150 and 250 N, three types of foam container ...
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Since persimmon is a pressure-sensitive fruit and it is difficult to store this fruit in warehouses, in this research, an attempt has been made to examine the parameters affecting the reduction of changes in its physical properties. The samples were loaded at 150 and 250 N, three types of foam container packaging with polyolefin film, polyethylene-terephthalate, and ordinary box, and four types of polyamine putrescine coating with concentrations of 1 and 2 mM, distilled water and uncoated. Properties such as Physiological Weight Loss, volume, and the density of persimmon fruit, as well as the firmness of this fruit in the prepost-storage stage were examined. The results showed, the highest firmness was obtained in the treatment of putrescine at a concentration of 1 mM and a foam container with polyethylene film with a value of 6.5 N, which was almost three times the firmness of uncoated fruits. The lowest Physiological Weight Loss, volume, and density were obtained in the same type of coating and packaging. The values of these parameters were 2.458%, 1.82, and 0.833%, respectively, compared to the first day of storage. Overall, the use of polyamine treatment showed a significant effect on changes in the physical properties of persimmon fruit, and foam containers with polyolefin film emerged as the optimal packaging option, resulting in the least amount of change among the different types of packaging used.
Food Engineering
Sajad Jafarzadeh; Mohsen Azadbakht; Faryal Varasteh; Mohammad Vahedi Torshizi
Abstract
Nowadays, the quality of processed fruits or products is defined by a set of physical and chemical properties. In this study, due to the sensitivity of persimmon fruit to pressure, the parameters affecting the color changes of this fruit after pressure have been investigated. Three different coatings ...
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Nowadays, the quality of processed fruits or products is defined by a set of physical and chemical properties. In this study, due to the sensitivity of persimmon fruit to pressure, the parameters affecting the color changes of this fruit after pressure have been investigated. Three different coatings and packing materials and two loading were applied to study the color changes of the samples. Samples were stored in the refrigerator for 25 days. According to the results obtained for the value of L*, b*, Chroma index, Hue index and color changes, the use of 1 mM polyamine coating had a significant effect and caused less change than other coatings. Foil container packaging with polyolefin film has also been better packaged. The lowest percentage reduction for L*, a*, b* , Chroma index and Hue index values was obtained in the 1 mM polyamine with a value of 8.26%, -26.43%, 12.35%, 1.31% and 120.995% respectively, Also the highest value was obtained in the uncoated state with a value of 18.49%, 73.32%, 19.84%, 15.95%, 152.36%. Finally, polyamine coating treatment has a positive effect to prevent the percentage reduction of color parameters of samples. The best coating treatment was polyamine with a concentration of 1 mM
Mohsen Azadbakht; Mohammad Vahedi Torshizi; Mohammad Javad Mahmoodi
Abstract
This research was aimed to study the effects of loading force and storage period on the physiological characteristic of pears. In this experiment, the pears were subjected to quasi-static loading (wide-edge and thin-edge) and different storage periods (5, 10 and 15 days). The amounts of the fruits’ ...
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This research was aimed to study the effects of loading force and storage period on the physiological characteristic of pears. In this experiment, the pears were subjected to quasi-static loading (wide-edge and thin-edge) and different storage periods (5, 10 and 15 days). The amounts of the fruits’ total phenol, antioxidant and vitamin C contents were evaluated after each storage period. In the present study, multilayer perceptron (MLP) artificial neural network featuring a hidden layer and two activating functions (hyperbolic tangent-sigmoid) and a total number of 5 and 10 neurons in each layer were selected for the loading force and storage period so that the amounts of the total phenol, antioxidants and vitamin C contents of the fruits could be forecasted. According to the obtained results, the highest R2 rates for thin-edge and wide-edge loading in a network with 10 neurons in the hidden layer and a sigmoid activation function were obtained for total phenol content( =0.9539- =0.9865), antioxidant ( =0.9839- =0.9649) and vitamin C ( =0.9758); as for wide-edge loading in a network with 5 neurons in the hidden layer and hyperbolic tangent activation function, the highest R2 rate of vitamin C content was obtained equal to =0.9865. According to the obtained results, the neural network with these two activation functions possesses an appropriate ability in overlapping and predicting the simulated data based on real data.
Mohammad Vahedi Torshizi; Mohsen Azadbakht
Abstract
This study evaluated the effect of different dynamic and static loadings and different storage periods on the firmness of pear fruit. Pear fruit was first segregated into three groups of 27 pear in order to undergo three loadings: static thin-edge compression loading, static wide-edge compression loading ...
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This study evaluated the effect of different dynamic and static loadings and different storage periods on the firmness of pear fruit. Pear fruit was first segregated into three groups of 27 pear in order to undergo three loadings: static thin-edge compression loading, static wide-edge compression loading and dynamic loading. All loaded pears were stored in accordance with three storage period designs: 5-day storage, 10-day storage, and 15-day storage. Following each period, the variations of pear texture were scanned by using the CT-Scan technique as a non-destructive test. Then, the firmness of pear texture was measured using a penetrometer. Data were simulated and evaluated using MLP and RBF artificial neural networks. The results showed that with increasing storage time and loading force , the firmness significantly decreased (1% level) in all three types of loading, In addition, pear texture was destructed under dynamic compression loading in order to compare with other two loadings. Best value artificial neural network for wide edge loading (12 neuron-RBF) was (R2 Wide edge= 0.9738– RMSE Wide edge=0.3419- MAE Wide edge =0.268) and for thin edge loading (4 neuron-RBF) was (R2Thin edge = 0.9946– RMSE Thin edge =0.170977- MAE Thin edge =0.133), also for dynamic loading (8 neuron-RBF) was (R2 Dynamic loading = 0.9933– RMSE Dynamic loading =0.230- MAE Dynamic loading= 0.187).
